• Water Engineering Research
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• v.5 no.2
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• pp.69-79
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• 2004

In this study the GFDL GCM generated (controlled run) zonal average temperature data are evaluated by comparing their EOFs with those from observed data. Even though the correlation matrices of observed and simulated data are shown significantly different (Polyak and North, 1997b), the EOFs derived are found very similar with very high pattern correlations. This means almost all the information (second-order statistics) derived from the observed data can be reproduced by the EOFs derived from the GFDL GCM simulates. Also, the EOFs from GFDL GCM were found to have more flexible structures than those from the observed. Thus, we may conclude that the GFDL GCM can simulate the Earth's energy balance system reasonably. However, more in detail research should be focused on the effect from various forcings on climate variability, as, in some cases, the effect of external forcings could shadow the system characteristics and mislead the simulation results.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.629-642
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• 2018

Future variability of the spatial patterns of rainfall events is the point of water-related risks and impacts of climate change. Recent related researches are mostly conducted based on the outcomes from General Circulation Models (GCMs), especially Coupled Model Intercomparison Project, phase 5 (CMIP5) GCMs which are the most advanced version of climate modeling system. GCM data have been widely used for various studies as the data utility keep getting improved. Meanwhile the model performances especially for raw GCM outputs are rarely evaluated prior to the applications although the process would essential for reasonable use of model forecasts. This study attempt to quantitatively evaluate the skills of 29 CMIP5 GCMs in reproducing spatial climatologies of precipitation in East Asia. We used 3 different gridded observational data as the references available over the study area and calculated correlation and errors of spatial patterns simulated by GCMs. As a result, the study presented diversity of the GCM evaluation in the performance, rank, or accuracy by different configurations, such as target area, evaluation method, and observation data. Yet, we found that Hadley-centre affiliated models comparatively performs better for the meso-scale area in East Asia and MPI_ESM_MR and CMCC family showed better performance specifically for the korean peninsula. We expect that the results and thoughts of this study would be considered in screening suitable GCMs for specific area, and finally contribute to extensive utilization of the results from climate change related researches.

• Proceedings of the Safety Management and Science Conference
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• 2012.11a
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• pp.331-335
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• 2012

본 연구에서는 물류분야에 MFCA를 도입하기 위해 공급업체와 본사간의 MFCA 정보를 공유할 수 있는 데이터베이스를 구축하여 원활한 정보흐름이 이루어지도록 하였다. 또한 GCM 프로세스를 분석하여 물류 부문에 MFCA를 적용할 수 있도록 물량센터를 물류 기능별 프로세스에 맞춰 선정하도록 하였다. 마지막으로 MFCA Framework를 5가지로 설정(MFCA Database Management, MFCA Visualization, GCM Data Integration, GCM Data Tracking, MFCA Data Predict & Assignment)하여 각 기능별로 설명하였다. 이를 통해 그동안 환경친화적 물류활동을 구현하는 기업들이 MFCA를 도입하고자 할 경우 GCM 프로세스 중 MFCA를 적용하는 범위 및 적용대상을 선정하는데 도움을 줄 수 있을 것으로 기대된다.

• Journal of the Korea Safety Management & Science
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• v.14 no.4
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• pp.147-152
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• 2012

Supply chain including transportation expend the 21% of domestic energy consumption. It is necessary to diminish the excess energy usage at entire supply chain. This paper deals with the application of MFCA(Material Flow Cost Accounting) for SCM to save energy consumption. We construct the material center corresponding to each logistics function in order to apply the MFCA for GCM(Green Chain Management). We also construct the MFCA framework which consists of MFCA Database Management, MFCA Visualization, GCM Data Integration, GCM Data Tracking, and MFCA Data Predict & Assignment. We expect to help determining the range of logistics function to apply the MFCA for GCM.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.6
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• pp.37-45
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• 2008

It is expected that conditions of water resources will be changed in Korea in accordance with world wide climate change. In order to deal with this problem and find a way of minimizing the effect of future climate change, the usefulness of climate model simulation information is examined in this study. The objective of this study is to assess the applicability of GCM (General Circulation Model) information for Korean water resources management through uncertainty analysis. The methods are based on probabilistic measures of the effectiveness of GCM simulations of an indicator variable for discriminating high versus low regional observations of a target variable. The formulation uses the significance probability of the Kolmogorov-Smirnov test for detecting differences between two variables. An estimator that accounts for climate model simulation and spatial association between the GCM data and observed data is used. Atmospheric general circulation model (AGCM) simulations done by ECMWF (European Centre for Medium-Range Weather Forecasts) with a resolution of $2^{\circ}{\times}2^{\circ}$, and METRI (Meteorological Research Institute, Korea) with resolutions of $2^{\circ}{\times}2^{\circ}$ and $4^{\circ}{\times}5^{\circ}$, were used for indicator variables, while observed mean areal precipitation (MAP) data, discharge data and mean areal temperature data on the seven major river basins in Korea were used for target variables. The results show that GCM simulations are useful in discriminating the high from the low of the observed precipitation, discharge, and temperature values. Temperature especially can be useful regardless of model and season.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.307-307
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• 2019

General Ciculation Model (GCM) 모형에 대한 평가를 본 연구에서 수행한다. 모형의 적용을 위해서는 국지적 일 강우량 및 기온자료를 이용한다. 31개의 GCM 모의를 통해 도출되는 결과가 성능 평가에서 활용되었다. 일 최대, 최소 기온와 강우량이 파키스탄 지역을 대상으로 모의되었다. 모의를 위해서는 Gridded 데이터가 적용되었으며 각각 Asian Precipitation-Highly-Resolved Observational Data Integration Toward Evaluation, Berkeley Earth Surface Temperature, Princeton Global Meteorological Forcing, Climate Prediction Centre에 해당된다. GCM의 순위를 결정하기 위해서는 Symmetrical Uncertainty 방법이 이용된다. 결과를 통해서 Gridded 데이터의 종류에 따라 가장 높은 효율을 나타내는 GCM의 공간 분포가 달라진다는 점을 확인하였다. 이러한 특성은 기온과 강우량 자료 모두에서 확인된다. 기온의 경우에는 Commonwealth Scientific and Industrial Research Organization, Australia-MK3-6-0과 Max Planck Institute-ESM-LR이 우수한 결과를 모의하는 것으로 나타났다. 반면 강우량의 경우에는 EC-Earth와 MIROC가 우수한 것으로 나타났다. 파키스탄 지역에서의 기온 및 강우량 자료의 합리적 반영을 위해서는 ACCESS1-3, CESM1-BGC, CMCC-CM, HadGEM2-CC, HadGEM2-ES, MIRCO5와 같은 6개 GCM을 이용하였을 때 다양한 기상 인자를 고려한 모의가 가능한 것으로 평가된다.

• Journal of Korea Water Resources Association
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• v.53 no.9
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• pp.647-660
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• 2020

The objectives of this study are to develop a framework for selecting multi-GCMs considering Asia monsoon characteristics and assess it's applicability. 12 climate variables related to monsoon climates are selected for GCM selection. The framework for selecting multi-GCMs includes the evaluation matrix of GCM performance based on their capability to simulate historical climate features. The climatological patterns of 12 variables derived from individual GCM over the summer monsoon season during the past period (1976-2005) and they are compared against observations to evaluate GCM performance. For objective evaluation, a rigorous scoring rule is implemented by comparing the GCM performance based on the results of statistics between historical simulation derived from individual GCM and observations. Finally, appropriate 5 GCMs (NorESM1-M, bcc-csm1-m, CNRM-CM5, CMCC-CMS, and CanESM2) are selected in consideration of the ranking of GCM and precipitation performance of each GCM. The selected 5 GCMs are compared with the historical observations in terms of monsoon season and monthly mean to validate their applicability. The 5 GCMs well capture the observational climate characteristics of Asia for the 12 climate variables also they reduce the bias between the entire GCM simulations and the observational data. This study demonstrates that it is necessary to consider various climate variables for GCM selection and, the method introduced in this study can be used to select more reliable climate change scenarios for climate change assessment in the Asia region.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.5
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• pp.69-80
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• 2015

The majority of projections of future climate come from Global Circulation Models (GCMs), which vary in the way they were modeled the climate system, and so it produces different projections about conceptualizing of the weather system. To implement climate change impact assessment, it is necessary to analyze trends of various GCMs and select appropriate GCM. In this study, climate data in 25 GCMs 41 outputs provided by Coupled Model Intercomparison Project Phase 5 (CMIP5) was downscaled at eight stations. From preliminary analysis of variations in projected temperature, precipitation and evapotranspiration, five GCM outputs were identified as candidates for the climate change impact analysis as they cover wide ranges of the variations. Also, GCM outputs are compared with trends of HadGCM3-RA, which are established by the Korean Meteorological Administration. From the results, it can contribute to select appropriate GCMs and to obtain reasonable results for the assessment of climate change.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.1
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• pp.119-126
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• 2020

This study analyzed the effects of future climate change on water resources in the Gyeongan-cheon watershed of the Han River. Considering the uncertainties of GCM climate data, future data using 16 GCMs and SQM downscaling method are used. And SWAT model was applied to simulate the hydrological changes from the past to the future. The maximum to minimum difference according to GCM for the future period (2010-2099) was about 1,500 mm of annual precipitation, 150 mm of evapotranspiration, 1,380 mm of runoff, and the deviation from the mean was -40 % to +60 % of precipitation, ±15 % of evapotranspiration, -60 % to +90 % of runoff, which means that the variability is very high according to GCM. The impacts of climate change over the three future periods showed that precipitation, evapotranspiration, and runoff were expected to increase gradually toward the far future (2070-2099), and would be relatively larger under the RCP 8.5 scenario. On a monthly basis, it was analyzed that precipitation and runoff increased in July to September, while the evapotranspiration decreased in July and August, and increased in September and October. The results of this study are expected to be helpful in understanding the future climate impacts of various GCM data and the uncertainties associated with GCMs.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.83-96
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• 2018

The objective of this study was to estimate the climate change impact on water quantity and quality to Saemanguem watershed using SWAT (Soil and water assessment tool) model. The SWAT model was calibrated and validated using observed data from 2008 to 2017 for the study watershed. The $R^2$ (Determination coefficient), RMSE (Root mean square error), and NSE (Nash-sutcliffe efficiency coefficient) were used to evaluate the model performance. RCP scenario data were produced from 10 GCM (General circulation model) and all relevant grid data including the major observation points (Gusan, Jeonju, Buan, Jeongeup) were extracted. The systematic error evaluation of the GCM model outputs was performed as well. They showed various variations based on analysis of future climate change effects. In future periods, the MIROC5 model showed the maximum values and the CMCC-CM model presented the minimum values in the climate data. Increasing rainfall amount was from 180mm to 250mm and increasing temperature value ranged from 1.7 to $5.9^{\circ}C$, respectively, compared with the baseline (2006~2017) in 10 GCM model outputs. The future 2030s and 2070s runoff showed increasing rate of 16~29% under future climate data. The future rate of change for T-N (Total nitrogen) and T-P (Total phosphorus) loads presented from -26 to +0.13% and from +5 to 47%, respectively. The hydrologic cycle and water quality from the Saemanguem headwater were very sensitive to projected climate change scenarios so that GCM model should be carefully selected for the purpose of use and the tendency analysis of GCM model are needed if necessary.