• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.529-534
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• 2009

Climate models, both global and regional, have increased in sophistication and are being run at increasingly higher resolutions. The Land Surface Models (LSMs) coupled to these climate models have evolved from simple bucket models to sophisticated Soil-Vegetation-Atmosphere Transfer (SVAT) schemes needed to support complex linkages and processes. However, some underpinnings of terrestrial hydrologic parameterizations so crucial in the predictions of surface water and energy fluxes cause model errors that often manifest as non-linear drifts in the dynamic response of land surface processes. This requires the improved parameterizations of key processes for the terrestrial hydrologic scheme to improve the model predictability in surface water and energy fluxes. The Common Land Model (CLM), one of state-of-the-art LSMs, is the land component of the Community Climate System Model (CCSM). However, CLM also has energy and water biases resulting from deficiencies in some parameterizations related to hydrological processes. This research presents the implementation of a selected set of parameterizations and their effects on the runoff prediction. The modifications consist of new parameterizations for soil hydraulic conductivity, water table depth, frozen soil, soil water availability, and topographically controlled baseflow. The results from a set of offline simulations are compared with observed data to assess the performance of the new model. It is expected that the advanced terrestrial hydrologic scheme coupled to the current CLM can improve model predictability for better prediction of runoff that has a large impact on the surface water and energy balance crucial to climate variability and change studies.

• Journal of Environmental Impact Assessment
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• v.12 no.1
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• pp.23-34
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• 2003

The emergency response modeling system CARIS has been developed at CCSM (Center for Chemical Safety Management), NIER (National Institute of Environmental Research) to track and predict dispersion of hazardous chemicals for the environmental decision support in case of accidents at chemical or petroleum companies in Korea. The main objective of CARIS is to support making decision by rapidly providing the key information on the efficient emergency response of hazardous chemical accidents for effective approaches to risk management. In particular, the integrated modeling system in CARIS consisting of a real-time numerical weather forecasting model and air pollution dispersion model is supplemented for the diffusion forecasts of hazardous chemicals, covering a wide range of scales and applications for atmospheric information. In this paper, we introduced the overview of components of CARIS and described the operational modeling system and its configurations of coupling/integration in CARIS. Some examples of the operational modeling system is presented and discussed for the real-time risk assessments of hazardous chemicals.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.229-229
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• 2015

Downscaling is a fundamental procedure in the assessment of the future climate change impact at regional and watershed scales. Hence, it is important to investigate the spatial variability of the climate conditions that are constructed by various downscaling methods in order to assess whether each method can model the climate conditions at various spatial scales properly. This study introduces a fundamental research from Jang and Kavvas(2015) that precipitation variability from a popular statistical downscaling method (BCSD) and a dynamical downscaling method (MM5) that is based on the NCAR/NCEP reanalysis data for a historical period and on the CCSM3 GCM A1B emission scenario simulations for a projection period, is investigated by means of some spatial characteristics: a) the normalized standard deviation (NSD), and b) the precipitation change over Northern California region. From the results of this study it is found that the BCSD method has limitations in projecting future precipitation values since the BCSD-projected precipitation, being based on the interpolated change factors from GCM projected precipitation, does not consider the interactions between GCM outputs and local geomorphological characteristics such as orographic effects and land use/cover patterns. As such, it is not clear whether the popular BCSD method is suitable for the assessment of the impact of future climate change at regional, watershed and local scales as the future climate will evolve in time and space as a nonlinear system with land-atmosphere feedbacks. However, it is noted that in this study only the BCSD procedure for the statistical downscaling method has been investigated, and the results by other statistical downscaling methods might be different.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.306-306
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• 2019

전지구적 기온상승으로 인해 미래기후의 관한 연구가 중요시 되고 있다. 위와 같은 현상으로 인하여 다양한 기후변화 연구가 진행되고 있다. 미래기후 연구에는 GCM (General Circulation Model) 모의 결과가 이용된다. 격자 자료로 구성된 GCM은 연구 지점으로 지역적 상세화와 연구지역의 관측자료 사이의 편이 보정(bias correction)이 필수적이다. 위와 같은 근거로 편이 보정 방법의 선택은 매우 중요하며 편의 보정의 방법에 따라서 결과가 다르게 도출될 수 있다. 또한 국내외 연구에서는 다양한 상세화 기법과 편이 보정 기법을 분석 및 평가하는 연구가 진행되고 있으며, 편의 기법 중 대표적인 기법인 Quantile mapping과 Random Forest 기법이 있다. Quantile mapping 기법은 GCM의 과거 모의 데이터와의 편이 보정에 있어서 우수하게 나타났으나, GCM 데이터의 미래 예측 기간(2010년~2018년)까지의 데이터에서는 극한 강수를 정량적으로 분석 가능한 Random Forest 기법이 편이 보정 과정에서 성능이 우수할 것으로 판단된다. 본 연구에서는 우리나라 21개 관측소를 기준으로 총 4개의 GCM(GISS, CSIRO, CCSM4,MIROC5)의 과거 기간 자료(1970년~2005년)를 실제 관측소에서 관측된 강수량을 편의 보정하는 방법에 있어서 편의 보정 기법의 성능을 비교한 결과와 GCM 미래 예측 기간 자료(2010년~2018년)에서의 편의 보정 기법의 성능 결과를 비교하였다. 이를 토대로 편이 보정 기법의 결과를 6개의 평가지수를 이용하여 정량적으로 분석하였으며, 다기준의사결정기법인 TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution)를 이용하여 편이 보정기법들의 성능에 있어서 우선순위를 선정하였다. 본 연구에서 편이 보정 방법으로 Quantile mapping 방법을 사용했으며, Quantile mapping의 기법으로는 비모수 변환법(non-parametric transformation)과 분포기반 변환법(distribution derived transformation)이 사용되었다. 또한 머신러닝 방법 중 하나인 Random Forest 방법을 동시에 사용하여 결과를 비교하였다. 또한 GCM 자료가 격자식으로 제공하고 있기 때문에 관측소 강수량도 공간적으로 환산하여야 하는데, 본 연구에서는 역거리 가중치법(inverse distance weighting, IDW) 방법을 이용하였다.

• Journal of Climate Change Research
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• v.1 no.1
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• pp.31-50
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• 2010

The Arctic climate change for the Last Glacial Maximum(LGM) occurred at 21,000 years ago (21ka) was investigated using simulation results of atmosphere-ocean coupled models from the second phase of the Paleoclimate Modelling Intercomparison Program(PMIP2). In the analysis, we used seven models, the NCAR CCSM of USA, ECHAM3-MPIOM of German Max-Planxk Institute, HadCM3M2 of UK Met Office, IPSL-CM4 of France Laplace Institute, CNRM-CM3 of France Meteorological Institute, MIROC3.2 of Japan CCSR at University of Tokyo, and FGOALS of China Institute of Atmospheric Physics. All the seven models reproduces the Arctic climate features found in the present climate at 0ka(pre-industrial time) in a reasonable degree in comparison to observations. During the LGM, the atmospheric $CO_2$ concentration and other greenhouse gases were reduced, the ice sheets were expanded over North America and northern Europe, the sea level was lowered by about 120m, and orbital parameters were slightly different. These boundary conditions were implemented to simulated LGM climate. With the implemented LGM conditions, the biggest temperature reduction by more than $24^{\circ}C$ is found over North America and northern Europe owing to ice albedo feedback and the change in lapse rate by high elevation. Besides, the expansion of ice sheets leads to the marked temperature reduction by more then $10^{\circ}C$ over the Arctic Ocean. The temperature reduction in northern winter is larger than in summer around the Arctic and the annual mean temperature is reduced by about $14^{\circ}C$. Compared to low mid-latitudes, the temperature reduction is much larger in high northern altitudes in the LGM. This results mirror the larger warming around the Artic in recent century. We could draw some information for the future under global warming from the knowledge of the LGM.